Can Population Data Help Predict Suicide Risk in Different Counties?

What you need to know

• Researchers developed a model to predict county-level suicide risk in the US using population characteristics and socioeconomic factors.
• The model found that higher firearm ownership, unemployment, and depression rates were associated with increased suicide risk.
• Higher median household income and population density were associated with decreased suicide risk.
• The gap in suicide risk between high-risk and low-risk counties widened from 2005 to 2016.
• This type of model could help public health agencies identify areas at higher risk and target suicide prevention efforts.

Understanding suicide risk at the population level

Suicide is a major public health concern in the United States. Suicide rates have increased by over 30% in the past two decades, making it one of the top 10 causes of death. While many efforts to prevent suicide focus on identifying individuals at high risk, looking at risk factors across entire populations can also provide valuable insights.

This study aimed to develop a model to predict suicide risk at the county level across the US. The researchers used data on population characteristics and socioeconomic factors to estimate how these variables relate to suicide rates. Understanding patterns of suicide risk across different areas could help public health officials target prevention efforts more effectively.

How the researchers built their model

The research team gathered data on all reported deaths by suicide in the US from 2005 to 2019. They also collected county-level data on factors that previous research has linked to suicide risk, including:

• Unemployment rates
• Average weekly wages
• Poverty rates
• Median household income
• Population density
• Rates of firearm ownership (at the state level)
• Rates of major depression (at the state level)

To analyze this data, the researchers used a statistical approach called conditional autoregressive modeling. This method accounts for the fact that suicide rates in neighboring counties and in consecutive years tend to be more similar to each other. By incorporating this spatial and temporal relationship, the model can provide more accurate estimates.

Key findings on suicide risk factors

The model revealed several important patterns in how population characteristics relate to county-level suicide risk:

Factors associated with increased risk:

• Firearm ownership: For every 11% increase in the rate of households owning firearms in a state, the model estimated a 2.8% increase in suicide risk.

• Major depression: A 0.7% increase in the prevalence of major depressive episodes was associated with a 1% increase in suicide risk.

• Unemployment: For every 3% increase in the unemployment rate, suicide risk increased by 2.8%.

Factors associated with decreased risk:

• Median household income: For every $12,000 increase in a county’s median household income, suicide risk decreased by 4.3%.

• Population density: Counties with higher population density (more urban areas) tended to have lower suicide risk. For each additional 5.8 people per square mile, risk decreased by 4.3%.

The relationship between average weekly wages and suicide risk was less clear, with only a small and statistically insignificant decrease in risk associated with higher wages.

Trends in suicide risk over time

In addition to identifying risk factors, the model also revealed important trends in suicide risk across the US from 2005 to 2016:

1. Overall increase in risk: The estimated national median suicide risk increased by about 27% during this period.

2. Widening gap between high-risk and low-risk areas: The difference in suicide risk between counties at the high and low ends of the spectrum grew larger over time. This suggests that some areas are experiencing much faster increases in suicide risk than others.

How this research could help prevent suicides

The model developed in this study could be a valuable tool for public health agencies working to prevent suicides. By estimating suicide risk at the county level, it can help identify areas that may need more resources or targeted interventions. For example:

1. Targeting high-risk areas: Counties with consistently high estimated risk could be prioritized for suicide prevention programs and mental health resources.

2. Identifying concerning trends: The model could flag counties experiencing rapid increases in suicide risk, allowing for early intervention.

3. Evaluating interventions: By tracking changes in estimated risk over time, public health officials could assess whether prevention efforts are having an impact.

4. Informing policy decisions: Understanding how factors like firearm ownership and economic conditions relate to suicide risk could guide policy decisions at the state and local levels.

Limitations and future directions

While this model provides valuable insights, it’s important to note some limitations:

1. Data availability: Some risk factors, like depression and firearm ownership rates, were only available at the state level rather than for individual counties.

2. Potential underreporting: Suicide deaths may be underreported or misclassified in some cases, particularly for certain racial or ethnic groups.

3. Annual estimates: The current model provides yearly estimates, which may not capture more rapid changes in risk.

To make this type of model even more useful for suicide prevention efforts, future research could focus on:

1. Real-time data: Incorporating more frequent updates on risk factors could allow for more timely risk estimates.

2. Additional risk factors: Including other relevant factors like social cohesion, access to mental health care, or environmental characteristics could improve the model’s accuracy.

3. Subgroup analysis: Developing separate models for different age groups or demographics could provide more targeted insights.

Conclusions

• This study demonstrates that population-level data can be used to estimate suicide risk across different areas.
• Factors like firearm ownership, economic conditions, and depression rates play important roles in determining suicide risk at the county level.
• The widening gap in suicide risk between high-risk and low-risk counties highlights the need for targeted prevention efforts.
• With further development, this type of model could become a valuable tool for public health agencies working to reduce suicide rates.